OpenRaiser/Intern-Atlas huggingface dataset:
SwiftLearn: A Data-Efficient Training Method of Deep Learning Models using Importance Sampling
Venue: arXiv cs.LG 2023
Year: 2023

In this paper, we present SwiftLearn, a data-efficient approach to accelerate training of deep learning models using a subset of data samples selected during the warm-up stages of training. This subset is selected based on an importance criteria measured over the entire dataset during warm-up stages, aiming to preserve the model performance with fewer examples during the rest of training. The importance measure we propose could be updated during training every once in a while, to make sure that all of the data samples have a chance to return to the training loop if they show a higher importance. The model architecture is unchanged but since the number of data samples controls the number of forward and backward passes during training, we can reduce the training time by reducing the number of training samples used in each epoch of training. Experimental results on a variety of CV and NLP models during both pretraining and finetuning show that the model performance could be preserved while achieving a significant speed-up during training. More specifically, BERT finetuning on GLUE benchmark shows that almost 90% of the data can be dropped achieving an end-to-end average speedup of 3.36x while keeping the average accuracy drop less than 0.92%.
Domain Knowledge Injection in Bayesian Search for New Materials
Venue: arXiv cs.AI 2023
Year: 2023

In this paper we propose DKIBO, a Bayesian optimization (BO) algorithm that accommodates domain knowledge to tune exploration in the search space. Bayesian optimization has recently emerged as a sample-efficient optimizer for many intractable scientific problems. While various existing BO frameworks allow the input of prior beliefs to accelerate the search by narrowing down the space, incorporating such knowledge is not always straightforward and can often introduce bias and lead to poor performance. Here we propose a simple approach to incorporate structural knowledge in the acquisition function by utilizing an additional deterministic surrogate model to enrich the approximation power of the Gaussian process. This is suitably chosen according to structural information of the problem at hand and acts a corrective term towards a better-informed sampling. We empirically demonstrate the practical utility of the proposed method by successfully injecting domain knowledge in a materials design task. We further validate our method's performance on different experimental settings and ablation analyses.
Estimation of the User Contribution Rate by Leveraging Time Sequence in Pairwise Matching function-point between Users Feedback and App Updating Log
Venue: arXiv cs.SE 2023
Year: 2023

Mobile applications have become an inseparable part of people's daily life. Nonetheless, the market competition is extremely fierce, and apps lacking recognition among most users are susceptible to market elimination. To this end, developers must swiftly and accurately apprehend the requirements of the wider user base to effectively strategize and promote their apps' orderly and healthy evolution. The rate at which general user requirements are adopted by developers, or user contribution, is a very valuable metric that can be an important tool for app developers or software engineering researchers to measure or gain insight into the evolution of app requirements and predict the evolution of app software. Regrettably, the landscape lacks refined quantitative analysis approaches and tools for this pivotal indicator. To address this problem, this paper exploratively proposes a quantitative analysis approach based on the temporal correlation perception that exists in the app update log and user reviews, which provides a feasible solution for quantitatively obtaining the user contribution. The main idea of this scheme is to consider valid user reviews as user requirements and app update logs as developer responses, and to mine and analyze the pairwise and chronological relationships existing between the two by text computing, thus constructing a feasible approach for quantitatively calculating user contribution. To demonstrate the feasibility of the approach, this paper collects data from four Chinese apps in the App Store in mainland China and one English app in the U.S. region, including 2,178 update logs and 4,236,417 user reviews, and from the results of the experiment, it was found that 16.6%-43.2% of the feature of these apps would be related to the drive from the online popular user requirements.
SpliceMix: A Cross-scale and Semantic Blending Augmentation Strategy for Multi-label Image Classification
Venue: arXiv cs.CV 2023
Year: 2023

Recently, Mix-style data augmentation methods (e.g., Mixup and CutMix) have shown promising performance in various visual tasks. However, these methods are primarily designed for single-label images, ignoring the considerable discrepancies between single- and multi-label images, i.e., a multi-label image involves multiple co-occurred categories and fickle object scales. On the other hand, previous multi-label image classification (MLIC) methods tend to design elaborate models, bringing expensive computation. In this paper, we introduce a simple but effective augmentation strategy for multi-label image classification, namely SpliceMix. The "splice" in our method is two-fold: 1) Each mixed image is a splice of several downsampled images in the form of a grid, where the semantics of images attending to mixing are blended without object deficiencies for alleviating co-occurred bias; 2) We splice mixed images and the original mini-batch to form a new SpliceMixed mini-batch, which allows an image with different scales to contribute to training together. Furthermore, such splice in our SpliceMixed mini-batch enables interactions between mixed images and original regular images. We also offer a simple and non-parametric extension based on consistency learning (SpliceMix-CL) to show the flexible extensibility of our SpliceMix. Extensive experiments on various tasks demonstrate that only using SpliceMix with a baseline model (e.g., ResNet) achieves better performance than state-of-the-art methods. Moreover, the generalizability of our SpliceMix is further validated by the improvements in current MLIC methods when married with our SpliceMix. The code is available at https://github.com/zuiran/SpliceMix.
Learning against Non-credible Auctions
Venue: arXiv cs.GT 2023
Year: 2023

The standard framework of online bidding algorithm design assumes that the seller commits himself to faithfully implementing the rules of the adopted auction. However, the seller may attempt to cheat in execution to increase his revenue if the auction belongs to the class of non-credible auctions. For example, in a second-price auction, the seller could create a fake bid between the highest bid and the second highest bid. This paper focuses on one such case of online bidding in repeated second-price auctions. At each time $t$, the winner with bid $b_t$ is charged not the highest competing bid $d_t$ but a manipulated price $p_t = α_0 d_t + (1-α_0) b_t$, where the parameter $α_0 \in [0, 1]$ in essence measures the seller's credibility. Unlike classic repeated-auction settings where the bidder has access to samples $(d_s)_{s=1}^{t-1}$, she can only receive mixed signals of $(b_s)_{s=1}^{t-1}$, $(d_s)_{s=1}^{t-1}$ and $α_0$ in this problem. The task for the bidder is to learn not only the bid distributions of her competitors but also the seller's credibility. We establish regret lower bounds in various information models and provide corresponding online bidding algorithms that can achieve near-optimal performance. Specifically, we consider three cases of prior information based on whether the credibility $α_0$ and the distribution of the highest competing bids are known. Our goal is to characterize the landscape of online bidding in non-credible auctions and understand the impact of the seller's credibility on online bidding algorithm design under different information structures.
OpenDigger: Data Mining and Information Service System for Open Collaboration Digital Ecosystem
Venue: arXiv cs.DL 2023
Year: 2023

The widespread development and adoption of open-source software have built an ecosystem for open development and collaboration. In this ecosystem, individuals and organizations collaborate to create high-quality software that can be used by everyone. Social collaboration platforms like GitHub have further facilitated large-scale, distributed, and fine-grained code collaboration and technical interactions. Countless developers contribute code, review code, report bugs, and propose new features on these platforms every day, generating a massive amount of valuable behavioral data from the open collaboration process. This paper presents the design and implementation of OpenDigger, a comprehensive data mining and information service system for open collaboration in the digital ecosystem. The goal is to build a data infrastructure for the open-source domain and promote the continuous development of the open-source ecosystem. The metrics and analysis models in the OpenDigger system can mine various knowledge from the macro to micro levels in the open-source digital ecosystem. Through a unified information service interface, OpenDigger provides various open-source information services to different user groups, including governments, enterprises, foundations, and individuals. As a novel information service system in the open-source ecosystem, this paper demonstrates the effectiveness of the metrics and models in OpenDigger through several real-world scenarios, including products, tools, applications, and courses. It showcases the significant and diverse practical applications of the metrics and models in both algorithmic and business aspects.
Probabilistic Transformer: A Probabilistic Dependency Model for Contextual Word Representation
Venue: arXiv cs.CL 2023
Year: 2023

Syntactic structures used to play a vital role in natural language processing (NLP), but since the deep learning revolution, NLP has been gradually dominated by neural models that do not consider syntactic structures in their design. One vastly successful class of neural models is transformers. When used as an encoder, a transformer produces contextual representation of words in the input sentence. In this work, we propose a new model of contextual word representation, not from a neural perspective, but from a purely syntactic and probabilistic perspective. Specifically, we design a conditional random field that models discrete latent representations of all words in a sentence as well as dependency arcs between them; and we use mean field variational inference for approximate inference. Strikingly, we find that the computation graph of our model resembles transformers, with correspondences between dependencies and self-attention and between distributions over latent representations and contextual embeddings of words. Experiments show that our model performs competitively to transformers on small to medium sized datasets. We hope that our work could help bridge the gap between traditional syntactic and probabilistic approaches and cutting-edge neural approaches to NLP, and inspire more linguistically-principled neural approaches in the future.
Double Reverse Regularization Network Based on Self-Knowledge Distillation for SAR Object Classification
Venue: arXiv cs.CV 2023
Year: 2023

In current synthetic aperture radar (SAR) object classification, one of the major challenges is the severe overfitting issue due to the limited dataset (few-shot) and noisy data. Considering the advantages of knowledge distillation as a learned label smoothing regularization, this paper proposes a novel Double Reverse Regularization Network based on Self-Knowledge Distillation (DRRNet-SKD). Specifically, through exploring the effect of distillation weight on the process of distillation, we are inspired to adopt the double reverse thought to implement an effective regularization network by combining offline and online distillation in a complementary way. Then, the Adaptive Weight Assignment (AWA) module is designed to adaptively assign two reverse-changing weights based on the network performance, allowing the student network to better benefit from both teachers. The experimental results on OpenSARShip and FUSAR-Ship demonstrate that DRRNet-SKD exhibits remarkable performance improvement on classical CNNs, outperforming state-of-the-art self-knowledge distillation methods.
Tessel: Boosting Distributed Execution of Large DNN Models via Flexible Schedule Search
Venue: arXiv cs.DC 2023
Year: 2023

Increasingly complex and diverse deep neural network (DNN) models necessitate distributing the execution across multiple devices for training and inference tasks, and also require carefully planned schedules for performance. However, existing practices often rely on predefined schedules that may not fully exploit the benefits of emerging diverse model-aware operator placement strategies. Handcrafting high-efficiency schedules can be challenging due to the large and varying schedule space. This paper presents Tessel, an automated system that searches for efficient schedules for distributed DNN training and inference for diverse operator placement strategies. To reduce search costs, Tessel leverages the insight that the most efficient schedules often exhibit repetitive pattern (repetend) across different data inputs. This leads to a two-phase approach: repetend construction and schedule completion. By exploring schedules for various operator placement strategies, Tessel significantly improves both training and inference performance. Experiments with representative DNN models demonstrate that Tessel achieves up to 5.5x training performance speedup and up to 38% inference latency reduction.
Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs
Venue: arXiv cs.CR 2023
Year: 2023

Organizations are increasingly recognizing the value of data collaboration for data analytics purposes. Yet, stringent data protection laws prohibit the direct exchange of raw data. To facilitate data collaboration, federated Learning (FL) emerges as a viable solution, which enables multiple clients to collaboratively train a machine learning (ML) model under the supervision of a central server while ensuring the confidentiality of their raw data. However, existing studies have unveiled two main risks: (i) the potential for the server to infer sensitive information from the client's uploaded updates (i.e., model gradients), compromising client input privacy, and (ii) the risk of malicious clients uploading malformed updates to poison the FL model, compromising input integrity. Recent works utilize secure aggregation with zero-knowledge proofs (ZKP) to guarantee input privacy and integrity in FL. Nevertheless, they suffer from extremely low efficiency and, thus, are impractical for real deployment. In this paper, we propose a novel and highly efficient solution RiseFL for secure and verifiable data collaboration, ensuring input privacy and integrity simultaneously.Firstly, we devise a probabilistic integrity check method that significantly reduces the cost of ZKP generation and verification. Secondly, we design a hybrid commitment scheme to satisfy Byzantine robustness with improved performance. Thirdly, we theoretically prove the security guarantee of the proposed solution. Extensive experiments on synthetic and real-world datasets suggest that our solution is effective and is highly efficient in both client computation and communication. For instance, RiseFL is up to 28x, 53x and 164x faster than three state-of-the-art baselines ACORN, RoFL and EIFFeL for the client computation.
Constructive validity of a generalized Kreisel-Putnam rule
Venue: arXiv cs.LO 2023
Year: 2023

In this paper, we propose a computational interpretation of the generalized Kreisel-Putnam rule, also known as the generalized Harrop rule or simply the Split rule, in the style of BHK semantics. We will achieve this by exploiting the Curry-Howard correspondence between formulas and types. First, we inspect the inferential behavior of the Split rule in the setting of a natural deduction system for the intuitionistic propositional logic. This will guide our process of formulating an appropriate program that would capture the corresponding computational content of the typed Split rule. In other words, we want to find an appropriate selector function for the Split rule by considering its typed variant. Our investigation can also be reframed as an effort to answer the following questions: is the Split rule constructively valid in the sense of BHK semantics? Our answer is positive for the Split rule as well as for its newly proposed generalized version called the S rule.
Local Convergence of Approximate Newton Method for Two Layer Nonlinear Regression
Venue: arXiv cs.LG 2023
Year: 2023

There have been significant advancements made by large language models (LLMs) in various aspects of our daily lives. LLMs serve as a transformative force in natural language processing, finding applications in text generation, translation, sentiment analysis, and question-answering. The accomplishments of LLMs have led to a substantial increase in research efforts in this domain. One specific two-layer regression problem has been well-studied in prior works, where the first layer is activated by a ReLU unit, and the second layer is activated by a softmax unit. While previous works provide a solid analysis of building a two-layer regression, there is still a gap in the analysis of constructing regression problems with more than two layers. In this paper, we take a crucial step toward addressing this problem: we provide an analysis of a two-layer regression problem. In contrast to previous works, our first layer is activated by a softmax unit. This sets the stage for future analyses of creating more activation functions based on the softmax function. Rearranging the softmax function leads to significantly different analyses. Our main results involve analyzing the convergence properties of an approximate Newton method used to minimize the regularized training loss. We prove that the loss function for the Hessian matrix is positive definite and Lipschitz continuous under certain assumptions. This enables us to establish local convergence guarantees for the proposed training algorithm. Specifically, with an appropriate initialization and after $O(\log(1/ε))$ iterations, our algorithm can find an $ε$-approximate minimizer of the training loss with high probability. Each iteration requires approximately $O(\mathrm{nnz}(C) + d^ω)$ time, where $d$ is the model size, $C$ is the input matrix, and $ω< 2.374$ is the matrix multiplication exponent.
EulerMerge: Simplifying Euler Diagrams Through Set Merges
Venue: arXiv cs.CG 2023
Year: 2023

Euler diagrams are an intuitive and popular method to visualize set-based data. In a Euler diagram, each set is represented as a closed curve, and set intersections are shown by curve overlaps. However, Euler diagrams are not visually scalable and automatic layout techniques struggle to display real-world data sets in a comprehensible way. Prior state-of-the-art approaches can embed Euler diagrams by splitting a closed curve into multiple curves so that a set is represented by multiple disconnected enclosed areas. In addition, these methods typically result in multiple curve segments being drawn concurrently. Both of these features significantly impede understanding. In this paper, we present a new and scalable method for embedding Euler diagrams using set merges. Our approach simplifies the underlying data to ensure that each set is represented by a single, connected enclosed area and that the diagram is drawn without curve concurrency, leading to well formed and understandable Euler diagrams.
GAN-Based LiDAR Intensity Simulation
Venue: arXiv cs.CV 2023
Year: 2023

Realistic vehicle sensor simulation is an important element in developing autonomous driving. As physics-based implementations of visual sensors like LiDAR are complex in practice, data-based approaches promise solutions. Using pairs of camera images and LiDAR scans from real test drives, GANs can be trained to translate between them. For this process, we contribute two additions. First, we exploit the camera images, acquiring segmentation data and dense depth maps as additional input for training. Second, we test the performance of the LiDAR simulation by testing how well an object detection network generalizes between real and synthetic point clouds to enable evaluation without ground truth point clouds. Combining both, we simulate LiDAR point clouds and demonstrate their realism.
Lived experiences of online harm amongst marginalized and vulnerable individuals in support-seeking communities on Reddit
Venue: arXiv cs.HC 2023
Year: 2023

Online communities can serve as meaningful sources of social support, particularly for marginalized and vulnerable groups. Disclosure of personal information facilitates integration into online communities but may also expose individuals to harm, including cyberbullying and manipulation. To better understand negative user experiences resulting from self-disclosure in online conversations, we interviewed 25 participants from target populations on Reddit. Through thematic analysis, we outline the harm they experience, including damage to self- and group identities. We find that encountering online harm can worsen offline adversity. We discuss how users protect themselves and recover from harm in the context of current platform affordances, highlighting ongoing challenges. Finally, we explore design implications for a community-driven, bottom-up approach to enhance user well-being and safety.
Functional Diffusion
Venue: arXiv cs.CV 2023
Year: 2023

We propose a new class of generative diffusion models, called functional diffusion. In contrast to previous work, functional diffusion works on samples that are represented by functions with a continuous domain. Functional diffusion can be seen as an extension of classical diffusion models to an infinite-dimensional domain. Functional diffusion is very versatile as images, videos, audio, 3D shapes, deformations, \etc, can be handled by the same framework with minimal changes. In addition, functional diffusion is especially suited for irregular data or data defined in non-standard domains. In our work, we derive the necessary foundations for functional diffusion and propose a first implementation based on the transformer architecture. We show generative results on complicated signed distance functions and deformation functions defined on 3D surfaces.